“Even in Poverty, We’re Still Dancing”: Helpful Coping Strategies in Response to Anti-Black Racism and Its Impact on Well-Being
Bibliographic record
Abstract
Black resilience has been long documented in Black history; yet too few studies discuss the positive narratives of Black experiences. There is too little discussion of positive and protective factors that are present in the Black community to cope with anti-Black racism. In this qualitative research, we aimed to explore and describe effective coping strategies used in response to anti-Black racism to promote both individual and collective well-being in the Black community. Using a strengths-based approach, the objectives of the study were to 1) explore helpful coping strategies used by Black Canadians ( N = 22) in response to anti-Black racism; and 2) describe their impact on personal and collective well-being through interviews. Results illustrated that emotional collective coping , active individual coping and africultural coping were helpful for participants in dealing with anti-Black racism. These coping strategies had positive impacts on participants’ personal well-being (e.g., self-efficacy) and collective well-being (e.g., social connection). This research describes the internal strength and collective power that exists in the Black community, especially in the Canadian context. Information from this study reaffirms the presence of Black resilience amidst the face of race-based stressors and orients both practitioners and researchers to understand the protective factors that enhance the lives of Black people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".